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  2. Sora (text-to-video model) - Wikipedia

    en.wikipedia.org/wiki/Sora_(text-to-video_model)

    Sora is a text-to-video model developed by OpenAI. The model generates short video clips based on user prompts, and can also extend existing short videos. Sora was released publicly for ChatGPT Plus and ChatGPT Pro users in December 2024. [1] [2]

  3. Midjourney - Wikipedia

    en.wikipedia.org/wiki/Midjourney

    Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco-based independent research lab Midjourney, Inc. Midjourney generates images from natural language descriptions, called prompts, similar to OpenAI's DALL-E and Stability AI's Stable Diffusion.

  4. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    It announced Sora, a text-to-video model intended to create realistic videos from text prompts, and available to ChatGPT Plus and Pro users. [ 113 ] [ 114 ] Additionally, OpenAI launched the o1 model, which is designed to be capable of advanced reasoning through its chain-of-thought processing, enabling it to engage in explicit reasoning before ...

  5. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. [99] Potential mitigation strategies for detecting generative AI content include digital watermarking , content authentication , information retrieval , and machine ...

  6. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.

  7. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    Generally, in order to get an LLM to use tools, one must fine-tune it for tool-use. If the number of tools is finite, then fine-tuning may be done just once. If the number of tools can grow arbitrarily, as with online API services, then the LLM can be fine-tuned to be able to read API documentation and call API correctly.